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Angela Burrell

Next-generation Targeted Sequencing Panel for Verification of Bovine Parentage

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• Bovine parentage verification is a critical aspect of successful herd management.

• Due to its highly accurate and reproducible results, SNP genotyping is becoming an increasingly favored tool for parentage verification.

• Genotyping by Sequencing (GBS) on the Ion S5™ sequencing system, labs can test hundreds of samples and thousands of SNPs simultaneously.

• Options: • Targeted GBS • Restriction Enzyme Mediated GBS

Introduction

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Genotyping by Sequencing (GBS) Options

GBS

Targeted GBS

Restriction Enzyme Mediated (RAD-Seq)

Common features: • Complexity of genome reduced

• SNP discovery

Key Differences

Up to 100,000s Number of SNPs interrogated/sample Up to 5,000

Med (20-80%) Consistency of markers called between sample High >95%

Random Types of markers identified Targeted – user selected

Discovery Best fit for … Mapping/Screening

Up to 100,000s 200

Med (20-80%) High >95%

Random

Discovery

Targeted

Parentage Verification

Targeted GBS

Bovine Parentage Verification

Restriction Enzyme Mediated (RAD-Seq)

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10 ng gDNA input

(Extracted Nucleic Acid

or Direct Lysis)

AgriSeq Targeted GBS Workflow

Hands on time <3 hours 15 minutes 15 minutes 15 minutes

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• AgriSeq HTS Library Kit is a high-throughput, low-volume targeted library prep kit. • Panel: Bovine ISAG SNP Parentage Panel (2013)

• 200 amplicon (100 “core” + 100 “additional”) SNP panel for bovine parentage verification. • Markers selected by the International Society of Animal Genetics (ISAG)

• Library Prep Workflow ~ 6 hours.

AgriSeq Library Prep

Pool libraries

Significant reagent savings

Barcode samples

768 barcodes available

Normalize Bead-based

library normalization

Clean-up 2X AMPure bead clean-

ups

Genomic DNA

10 ng/rxn

PCR Ultra-high multiplex targeted

PCR

Pre-ligation treatment Enzyme

treatment to prepare for barcoding

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• Purpose: To evaluate the AgriSeq workflow with the Bovine ISAG SNP Parentage Panel (2013) on bovine parentage verification testing.

• 115 highly-diverse DNA samples in replicates (n=2 or n=4): • USDA (MARC Beef Cattle Diversity Panel v2.9) • 2015 ISAG/ICAR 3rd SNP Typing Bovine

Comparison Test

• N=384 libraries pooled on Ion ChefTM and Ion S5XL™ sequencing system using an Ion 540™ chip.

• Data analyzed with Torrent Suite Software • Torrent Variant Caller Plugin • Coverage Analysis Plugin

Experimental Set-up Breed Number

Angus 7 Beefmaster 5 Brahman 6 Brangus 6 Braunieh 4 Charolais 7 Chianina 5 Corriente 5 Gelbvieh 7 Herford 8

Limousin 6 Longhorn 5

Main-Anjou 6 Red Angus 7

Salers 6 Santa Gertrudis 5

Shorthorn 6 Simmental 9 Tarentaise 5

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• Call rate, the percent of markers generating a genotype call for a specific sample, was calculated for all samples tested.

• Mean call rate for the Bovine ISAG panel was 98.5%. • 114 out of 115 samples had mean call rates above 95%.

Mean Call Rate

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• Uniformity: Percentage of target bases covered by at least 0.2X of the average base read depth. • How evenly you are covering target amplicons with reads. • Low uniformity (<90%) can lead to marker drop-off and poor call rates.

• The mean read uniformity of the Bovine ISAG SNP Parentage Panel (2013) was 97.8%.

Mean Read Uniformity

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• An ANOVA was performed to determine whether there was a statistical difference in call rate between the 20 different cattle breeds tested.

• There was not a statistically significant difference in call rate between the different breeds.

Comparison Across Breeds

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• Ran a single sample in 96 replicates through the AgriSeq workflow. • Repeated on two separate days.

• Genotype concordance was calculated between the replicates on the same sequencing run and between replicates on separate sequencing runs.

• Both inter-run and intra-run concordance was >99.9%.

High Replicate Genotype Concordance

Metric Mean Genotyping Call Concordance Stdev

Intra-run Concordance 99.97% 0.13% Inter-run Concordance 99.99% 0.07%

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Analysis Metrics 2012 ISAG Guidelines (min. core markers) Bovine 9 and 21 Bovine 17 and 21

# of SNPs in reference core panel >100 200 (100 core) 200 (100 core)

# of SNPs in Available in Profile >95 200 (100 core) 197 (99 core)

# of common SNPs in verification offspring >90 200 (100 core) 197 (99 core)

# of Mismatches <1 0 17

• Two pairs of samples in the tested for parentage verification using the AgriSeq workflow and compared to the reported results.

• AgriSeq results were completely concordant with the 2015 ISAG/ICAR reported results.

2015 ISAG/ICAR Parentage Test Results

Results Bovine 9 and 21 Bovine 17 and 21 AgriSeq Results Parentage Verified Parentage Excluded

2015 ISAG/ICAR Reported Results Parentage Verified Parentage Excluded Concordance

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• Samples were hybridized to six replicate microarrays and four replicate GBS sequencing reactions to obtain consensus genotype calls.

• Concordance was calculated as the number of times the genotype call matched between samples run on the two different technologies divided by the total number of markers.

Microarray Concordance

Samples run on both technologies 44 Total number of markers 8800 Number of concordant calls 8751 Concordance (%) 99.4%

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• AgriSeq technology can identify additional SNPs that fall within the targeted amplicon regions. • Provide new markers for linkage analysis or enhance discrimination in

parentage and traceability applications • 495 potential novel variants were identified at varying frequencies.

• Mean novel variants ~101/sample. • Recommend validating novel SNPs (qPCR).

Novel SNP Identification

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• The AgriSeq library prep workflow and Bovine ISAG SNP Parentage Panel (2013) provide a effective method for cattle parentage verification. • Mean call rate 98.5% • No bias between breeds • Run-to-run genotype concordance >99.9% • Array concordance 99.4%

• Up to 4X 384-well plates can be processed in a single day and full sequencing results can be obtained in as little as two days.

• While we demonstrated the utility of Ion Torrent sequencing technology for assessing parentage in cattle, our approach can be applied to other SNP genotyping problems as well.

Conclusions

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• Thermo Fisher Scientific AgriGenomics Team • Prasad Siddavatam (Bioinformatics) • Adam Allred (Bioinformatics) • Michelle Swimley (R&D) • Chris Willis (R&D) • Rick Conrad (R&D) • Christina Buchanan-Wright (Business)

• Ryan Ferretti- Neogen Corporation (R&D)

• We would like to acknowledge Michael Heaton at the USDA for supplying the MARC Beef Cattle Diversity Panel v2.9 used in this study and Louis Cancela for supplying the 2015 ISAG/ICAR DNA and reported results.

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Acknowledgements